Impact of diabetes on remodelling, microvascular function and exercise capacity in aortic stenosis

Objective To characterise cardiac remodelling, exercise capacity and fibroinflammatory biomarkers in patients with aortic stenosis (AS) with and without diabetes, and assess the impact of diabetes on outcomes. Methods Patients with moderate or severe AS with and without diabetes underwent echocardiography, stress cardiovascular magnetic resonance (CMR), cardiopulmonary exercise testing and plasma biomarker analysis. Primary endpoint for survival analysis was a composite of cardiovascular mortality, myocardial infarction, hospitalisation with heart failure, syncope or arrhythmia. Secondary endpoint was all-cause death. Results Diabetes (n=56) and non-diabetes groups (n=198) were well matched for age, sex, ethnicity, blood pressure and severity of AS. The diabetes group had higher body mass index, lower estimated glomerular filtration rate and higher rates of hypertension, hyperlipidaemia and symptoms of AS. Biventricular volumes and systolic function were similar, but the diabetes group had higher extracellular volume fraction (25.9%±3.1% vs 24.8%±2.4%, p=0.020), lower myocardial perfusion reserve (2.02±0.75 vs 2.34±0.68, p=0.046) and lower percentage predicted peak oxygen consumption (68%±21% vs 77%±17%, p=0.002) compared with the non-diabetes group. Higher levels of renin (log10renin: 3.27±0.59 vs 2.82±0.69 pg/mL, p<0.001) were found in diabetes. Multivariable Cox regression analysis showed diabetes was not associated with cardiovascular outcomes, but was independently associated with all-cause mortality (HR 2.04, 95% CI 1.05 to 4.00; p=0.037). Conclusions In patients with moderate-to-severe AS, diabetes is associated with reduced exercise capacity, increased diffuse myocardial fibrosis and microvascular dysfunction, but not cardiovascular events despite a small increase in mortality.


Supplementary Method: Image analysis
LV volumes and mass were quantified using the built-in automated contouring tool using the short-axis stack with adjustments only made for clear and obvious errors. Biplane left atrial volumes were calculated using the 4-and 2chamber cine images using the automated tool to contour throughout the cardiac cycle.
Tissue tracking was used to assess myocardial strain as previously described[1] to calculate global longitudinal strain (GLS) and global circumferential strain (GCS) as well as longitudinal and circumferential peak early diastolic strain rate (PEDSR). Systolic strain values are presented as absolute values such that lower values indicate worse myocardial mechanics [2]. Extracellular volume fraction (ECV) was calculated using pre-and post-contrast T1 maps and the haematocrit sampled on the same day as the CMR scan [3].
Perfusion images were first assessed qualitatively for regional perfusion defects by two experienced observers.
Quantitative perfusion was assessed using a model independent deconvolution technique[4], or a dual-sequence gradient echo method with inline automated reconstruction and post-processing [5]. Myocardial perfusion reserve (MPR) was calculated as a ratio of global stress to rest myocardial blood flow. To minimise the impact of epicardial disease on assessment of coronary microvascular function, participants with infarction, regional perfusion defects or known obstructive coronary artery disease were excluded from quantitative perfusion analysis.
LGE images were qualitatively assessed by two experienced observers for focal fibrosis which was categorised as present or absent. Where present it was further categorised as infarction or non-ischemic. Right ventricular insertion point fibrosis was not classed as pathological.

Supplementary Method: Plasma Biomarkers
Plasma biomarkers for which >80% of participants had values below the lower limit of detection were excluded from analysis. Plasma biomarkers where 10-80% of values were at the lower limit of detection were dichotomised by first removing the values at the lower limit of detection. The Log10 mean was then used as the threshold for the remaining values. Values below the lower limit of detection or below the Log10 mean were classed as "Low", whilst those above the Log10 mean were classed as "High". Biomarkers where <10% met the lower limit of detection were treated as continuous variables with data Log10 transformed prior to group comparison.